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Merging information for semiparametric density estimation

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The density ratio model specifies that the likelihood ratio of m−1 probability density functions with respect to the mth is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well-established techniques to choose the smoothing parameter for the density estimators proposed.
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Keywords: Bandwidth; Biased sampling; Discrete choice models; Empirical likelihood; Kernel estimator; Retrospective sampling

Document Type: Research Article

Affiliations: University of Cyprus, Nicosia, Cyprus

Publication date: November 1, 2004

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